Course

Centre for Science and Technology Studies

The Centre for Science and Technology Studies (CWTS) studies scientific research and its connections to technology, innovation, and society. Our research, bibliometric and scientometric tools, and evaluation expertise provide a solid basis for supporting research assessment and strategic decision making and for developing science policy.

At the @paulwouters Farewell Symposium: @jameswilsdon discusses the "cosmetic" appropriation of open science discourse by corporations and the negative political implications of this appropriation @cwtsleidenpic.twitter.com/JrVciXYsUn – bij Poortgebouw Veiligheid & Milieu

A few years ago Ton van Raan, emeritus professor of Quantitative Science Studies at CWTS, demonstrated that universities show a similar scaling behavior as cities in the distribution of the ‘gross university income’ in terms of total number of citations over ‘size’ in terms of total number of publications. Moreover, the power law exponents for university scaling are comparable to those for urban scaling.

The Centre for Science and Technology Studies (Leiden University) has adopted a new governance structure. As of January 1, 2019, the centre will be led by Prof. Sarah de Rijcke, who has been appointed by the board of the Faculty of Social and Behavioral Sciences (FSW) as the new scientific director for a period of 4 years.

Only a small minority of all scientific publications contain genuine scientific breakthroughs. It takes, in general, a considerable amount of time before a scientific discovery is recognized as a breakthrough. We show that combining decision heuristics with algorithms that analyse the response of the scientific community to a research publication enables the detection of breakthroughs at an early stage.

An exciting development in the field of quantitative science studies is the use of algorithmic clustering approaches to construct article-level classifications based on citation networks. Until recently, most classifications were based on categorizing journals rather than individual articles. This is understandable given the substantial challenges of classifying millions of articles. At CWTS, we now routinely work with article-level classifications. We have dedicated quite some time developing clustering algorithms for creating these classifications. These algorithms have an impact beyond our own research field and are of interest to many network scientists.